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Article

On the Data-Driven Modeling of Reactive Extrusion

1
PIMM, Arts et Métiers Institute of Technology, 151 Boulevard de l’Hôpital, 75013 Paris, France
2
Univ-Lyon, Université Lyon 1, Ingénierie des Matériaux Polymères, CNRS, UMR5223, 15 Boulevard André Latarjet, 69622 Villeurbanne, France
3
Notre Dame University-Louaize, P.O. Box 72, Zouk Mikael, Zouk Mosbeh 1211, Lebanon
4
LAMPA, Arts et Métiers Institute of Technology, 2 Boulevard du Ronceray, 49035 Angers, France
*
Author to whom correspondence should be addressed.
Fluids 2020, 5(2), 94; https://doi.org/10.3390/fluids5020094
Received: 2 June 2020 / Revised: 9 June 2020 / Accepted: 10 June 2020 / Published: 15 June 2020
(This article belongs to the Special Issue Advances in Experimental and Computational Rheology, Volume II)
This paper analyzes the ability of different machine learning techniques, able to operate in the low-data limit, for constructing the model linking material and process parameters with the properties and performances of parts obtained by reactive polymer extrusion. The use of data-driven approaches is justified by the absence of reliable modeling and simulation approaches able to predict induced properties in those complex processes. The experimental part of this work is based on the in situ synthesis of a thermoset (TS) phase during the mixing step with a thermoplastic polypropylene (PP) phase in a twin-screw extruder. Three reactive epoxy/amine systems have been considered and anhydride maleic grafted polypropylene (PP-g-MA) has been used as compatibilizer. The final objective is to define the appropriate processing conditions in terms of improving the mechanical properties of these new PP materials by reactive extrusion. View Full-Text
Keywords: reactive extrusion; data-driven; machine learning; artificial engineering; polymer processing; digital twin reactive extrusion; data-driven; machine learning; artificial engineering; polymer processing; digital twin
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MDPI and ACS Style

Ibañez, R.; Casteran, F.; Argerich, C.; Ghnatios, C.; Hascoet, N.; Ammar, A.; Cassagnau, P.; Chinesta, F. On the Data-Driven Modeling of Reactive Extrusion. Fluids 2020, 5, 94. https://doi.org/10.3390/fluids5020094

AMA Style

Ibañez R, Casteran F, Argerich C, Ghnatios C, Hascoet N, Ammar A, Cassagnau P, Chinesta F. On the Data-Driven Modeling of Reactive Extrusion. Fluids. 2020; 5(2):94. https://doi.org/10.3390/fluids5020094

Chicago/Turabian Style

Ibañez, Ruben, Fanny Casteran, Clara Argerich, Chady Ghnatios, Nicolas Hascoet, Amine Ammar, Philippe Cassagnau, and Francisco Chinesta. 2020. "On the Data-Driven Modeling of Reactive Extrusion" Fluids 5, no. 2: 94. https://doi.org/10.3390/fluids5020094

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